{"id":4464,"date":"2011-06-25T22:34:16","date_gmt":"2011-06-25T22:34:16","guid":{"rendered":"http:\/\/hgpu.org\/?p=4464"},"modified":"2011-06-25T22:34:16","modified_gmt":"2011-06-25T22:34:16","slug":"parallel-processing-for-normal-mixture-models-of-hyperspectral-data-using-a-graphics-processor","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4464","title":{"rendered":"Parallel Processing for Normal Mixture Models of Hyperspectral Data Using a Graphics Processor"},"content":{"rendered":"<p>Multivariate normal mixture models, where a complex statistical distribution is represented by a weighted sum of several multivariate normal probability distributions, have many potential applications including anomaly detection (AD) in hyperspectral (HS) images. The high computational cost of mixture models requires hardware and\/or algorithmic acceleration to make AD run in real time. In this paper we describe the concurrency present in the AD algorithm that includes a normal mixture estimation task. We explore the use of graphics processing units (GPUs) for parallel implementation of the algorithm. The GPU implementations provide a significant speedup compared to multi-core central processing unit (CPU) implementations, and enable the algorithm to execute in real time.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Multivariate normal mixture models, where a complex statistical distribution is represented by a weighted sum of several multivariate normal probability distributions, have many potential applications including anomaly detection (AD) in hyperspectral (HS) images. The high computational cost of mixture models requires hardware and\/or algorithmic acceleration to make AD run in real time. In this paper [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,89,33,3],"tags":[1787,14,1786,20,374],"class_list":["post-4464","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-image-processing","category-paper","tag-algorithms","tag-cuda","tag-image-processing","tag-nvidia","tag-nvidia-geforce-8800-ultra"],"views":2166,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4464","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4464"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4464\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4464"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4464"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4464"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}